
gp = 

            type: 'FULL'
             lik: [1x1 struct]
              cf: {[1x1 struct]}
    infer_params: 'covariance+likelihood'
    jitterSigma2: 1.0000e-09
              fh: [1x1 struct]

Gaussian noise model and MAP estimate for parameters
 TolFun reached. Func-count 23. Final f(x)=27.0141. Elapsed time 0.18

S1 =

length-scale: 1.066, magnSigma2: 5.420  


Scale mixture Gaussian (~=Student-t) noise model                
using MCMC integration over the latent values and parameters    
 Using SLS sampler for hyperparameters
 cycle  etr      slsrej  
   20  -38.105  sls  
   40  -51.018  sls  
   60  -54.371  sls  
   80  -54.989  sls  
  100  -61.803  sls  
  120  -65.385  sls  
  140  -50.697  sls  
  160  -64.071  sls  
  180  -47.628  sls  
  200  -53.372  sls  
  220  -55.422  sls  
  240  -55.152  sls  
  260  -59.509  sls  
  280  -45.947  sls  
  300  -53.208  sls  

S2 =

length-scale: 1.051, magnSigma2: 7.908 


Student-t noise model using Laplace integration over the 
latent values and MAP estimate for the parameters        
 TolFun reached. Func-count 27. Final f(x)=-24.079. Elapsed time 1.10

S3 =

length-scale: 1.047, magnSigma2: 3.092 


Student-t noise model using EP integration over the
latent values and MAP estimate for parameters      
  Iteration  Func-count      f(x)      Lambda
      0          1          5.0753    
      1          3       -0.390577       10
      2          5        -20.2665        5
      3          7        -21.8841      2.5
      4          9        -23.5458     1.25
      5         11        -23.9733    0.625
      6         13        -24.8948    0.312
      7         15           -25.2    0.156
      8         17        -25.3813   0.0781
      9         19        -25.4289   0.0391
     10         21        -25.5223   0.0195
     11         23        -25.5358   0.00977
     12         25        -25.6001   0.00488
     13         27         -25.619   0.00244
     14         29        -25.6518   0.00122
     15         31        -25.6526   0.00061
 TolFun reached. Func-count 31. Final f(x)=-25.6526. Elapsed time 17.56

S4 =

length-scale: 1.040, magnSigma2: 2.920 


Student-t noise model with nu= 4 and using MCMC integration
over the latent values and parameters                      
 Using SSLS sampler for hyperparameters and ESLS for latent values
 cycle  etr      lslsn
   20  -194.741  7.0e+00  
   40  -247.772  9.0e+00  
   60  -263.046  9.0e+00  
   80  -286.524  4.0e+00  
  100  -284.345  9.0e+00  
  120  -283.511  8.0e+00  
  140  -281.262  7.0e+00  
  160  -293.818  1.2e+01  
  180  -294.601  1.6e+01  
  200  -302.074  8.0e+00  
  220  -303.498  8.0e+00  
  240  -303.258  1.0e+01  
  260  -308.047  1.6e+01  
  280  -313.108  8.0e+00  
  300  -308.032  1.2e+01  
  320  -307.147  1.2e+01  
  340  -313.006  9.0e+00  
  360  -315.498  1.0e+01  
  380  -309.592  1.1e+01  
  400  -309.290  1.1e+01  

S5 =

length-scale: 0.761, magnSigma2: 2.366 


Student-t noise model with nu=4 using Laplace integration over
the latent values and MAP estimate for the parameters         
 TolFun reached. Func-count 19. Final f(x)=-14.9305. Elapsed time 0.51

S6 =

length-scale: 1.040, magnSigma2: 3.095 


Student-t noise model with nu=4 using EP integration over
the latent values and MAP estimate for parameters        
  Iteration  Func-count      f(x)      Lambda
      0          1          5.0753    
      1          3       -0.138385       10
      2          5        -13.6439        5
      3          7        -14.0719      2.5
      4          9        -14.1846     1.25
      5         11        -14.8554    0.625
      6         13        -15.1515    0.312
      7         15        -15.1629    0.156
      8         17        -15.1788   0.0781
      9         19        -15.1794   0.0391
 TolFun reached. Func-count 19. Final f(x)=-15.1794. Elapsed time 7.74

S7 =

length-scale: 1.033, magnSigma2: 2.966 



 gp hyperparameters: 
 
    1.0871    0.0321   -4.6357

Demo completed in 1.327 minutes
